27 research outputs found
Communication and Interference Coordination
We study the problem of controlling the interference created to an external
observer by a communication processes. We model the interference in terms of
its type (empirical distribution), and we analyze the consequences of placing
constraints on the admissible type. Considering a single interfering link, we
characterize the communication-interference capacity region. Then, we look at a
scenario where the interference is jointly created by two users allowed to
coordinate their actions prior to transmission. In this case, the trade-off
involves communication and interference as well as coordination. We establish
an achievable communication-interference region and show that efficiency is
significantly improved by coordination
A Beta-Beta Achievability Bound with Applications
A channel coding achievability bound expressed in terms of the ratio between
two Neyman-Pearson functions is proposed. This bound is the dual of a
converse bound established earlier by Polyanskiy and Verd\'{u} (2014). The new
bound turns out to simplify considerably the analysis in situations where the
channel output distribution is not a product distribution, for example due to a
cost constraint or a structural constraint (such as orthogonality or constant
composition) on the channel inputs. Connections to existing bounds in the
literature are discussed. The bound is then used to derive 1) an achievability
bound on the channel dispersion of additive non-Gaussian noise channels with
random Gaussian codebooks, 2) the channel dispersion of the exponential-noise
channel, 3) a second-order expansion for the minimum energy per bit of an AWGN
channel, and 4) a lower bound on the maximum coding rate of a multiple-input
multiple-output Rayleigh-fading channel with perfect channel state information
at the receiver, which is the tightest known achievability result.Comment: extended version of a paper submitted to ISIT 201
Beta-Beta Bounds: Finite-Blocklength Analog of the Golden Formula
It is well known that the mutual information between two random variables can
be expressed as the difference of two relative entropies that depend on an
auxiliary distribution, a relation sometimes referred to as the golden formula.
This paper is concerned with a finite-blocklength extension of this relation.
This extension consists of two elements: 1) a finite-blocklength channel-coding
converse bound by Polyanskiy and Verd\'{u} (2014), which involves the ratio of
two Neyman-Pearson functions (beta-beta converse bound); and 2) a novel
beta-beta channel-coding achievability bound, expressed again as the ratio of
two Neyman-Pearson functions.
To demonstrate the usefulness of this finite-blocklength extension of the
golden formula, the beta-beta achievability and converse bounds are used to
obtain a finite-blocklength extension of Verd\'{u}'s (2002) wideband-slope
approximation. The proof parallels the derivation of the latter, with the
beta-beta bounds used in place of the golden formula.
The beta-beta (achievability) bound is also shown to be useful in cases where
the capacity-achieving output distribution is not a product distribution due
to, e.g., a cost constraint or structural constraints on the codebook, such as
orthogonality or constant composition. As an example, the bound is used to
characterize the channel dispersion of the additive exponential-noise channel
and to obtain a finite-blocklength achievability bound (the tightest to date)
for multiple-input multiple-output Rayleigh-fading channels with perfect
channel state information at the receiver.Comment: to appear in IEEE Transactions on Information Theor
Channel Detection in Coded Communication
We consider the problem of block-coded communication, where in each block,
the channel law belongs to one of two disjoint sets. The decoder is aimed to
decode only messages that have undergone a channel from one of the sets, and
thus has to detect the set which contains the prevailing channel. We begin with
the simplified case where each of the sets is a singleton. For any given code,
we derive the optimum detection/decoding rule in the sense of the best
trade-off among the probabilities of decoding error, false alarm, and
misdetection, and also introduce sub-optimal detection/decoding rules which are
simpler to implement. Then, various achievable bounds on the error exponents
are derived, including the exact single-letter characterization of the random
coding exponents for the optimal detector/decoder. We then extend the random
coding analysis to general sets of channels, and show that there exists a
universal detector/decoder which performs asymptotically as well as the optimal
detector/decoder, when tuned to detect a channel from a specific pair of
channels. The case of a pair of binary symmetric channels is discussed in
detail.Comment: Submitted to IEEE Transactions on Information Theor
Improved detection in CDMA for biased sources
We consider the detection of biased information sources in the ubiquitous code-division multiple-access (CDMA) scheme. We propose a simple modification to both the popular single-user matched-filter detector and a recently introduced near-optimal message-passing-based multiuser detector. This modification allows for detecting modulated biased sources directly with no need for source coding. Analytical results and simulations with excellent agreement are provided, demonstrating substantial improvement in bit error rate in comparison with the unmodified detectors and the alternative of source compression. The robustness of error-performance improvement is shown under practical model settings, including bias estimation mismatch and finite-length spreading codes. © 2007 IOP Publishing Ltd